A note on limits of sequences of binary trees by Rudolf Grübel

“…For random trees the subtree size topology arises in the context of algorithms for searching and sorting when applied to random input, resulting in a sequence of nested trees. …”
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A Novel Tree-Based Combined Test for Seasonality by Karsten Webel, Daniel Ollech

Subjects: “…Binary classification…”
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Training multi-layer binary neural networks with random local binary error signals by Luca Colombo, Fabrizio Pittorino, Manuel Roveri

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Stability of Characteristics of Physical Random Number Generators by M. O. Pikuza, S. Yu. Mikhnevich, A. Yu. Siankevich

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Performance Comparison of Random Forest and Decision Tree Algorithms for Anomaly Detection in Networks by Rafiq Fajar Ramadhan, Wahid Miftahul Ashari

“…From the study result, it can be conclude that the Decision Tree algorithm performs better in detecting anomalies in binary data with an accuracy of 99,71%. …”
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Random Generalized Additive Logistic Forest: A Novel Ensemble Method for Robust Binary Classification by Oyebayo Ridwan Olaniran, Ali Rashash R. Alzahrani, Nada MohammedSaeed Alharbi, Asma Ahmad Alzahrani

“…We introduce a novel ensemble approach, the Random Generalized Additive Logistic Forest (RGALF), which integrates generalized additive models (GAMs) within a random forest framework to improve binary classification tasks. …”
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Failing Despite Knowing. Repeated Decision-Making During Binary Random Sequences with Asymmetric Probabilities by Uatkán Ajna, Ruzsa Gábor, Faragó Klára

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Evaluating the three-level approach of the U-smile method for imbalanced binary classification. by Barbara Więckowska, Katarzyna B Kubiak, Przemysław Guzik

“…Real-life binary classification problems often involve imbalanced datasets, where the majority class outnumbers the minority class. …”
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A Lower Bound on the Success Probability of Binary Random Linear Network Codes Aided by Noise Decoding by Ioannis Chatzigeorgiou

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Non-Fragile Estimation for Nonlinear Delayed Complex Networks with Random Couplings Using Binary Encoding Schemes by Nan Hou, Weijian Li, Yanhua Song, Mengdi Chang, Xianye Bu

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Analysis of motorcyclists crash severity using cluster correspondence and hierarchical binary logit models by Richard Dzinyela, Bahar Dadashova, Grant Westfall, Subasish Das, Chiara Silvestri-Dobrovolny, Emmanuel Kofi Adanu, Dominique Lord

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CREDIT CARD FRAUD DETECTION USING LINEAR DISCRIMINANT ANALYSIS (LDA), RANDOM FOREST, AND BINARY LOGISTIC REGRESSION by Muhammad Ahsan, Tabita Yuni Susanto, Tiza Ayu Virania, Andi Indra Jaya

“…In this research, we describe fraud detection as a classification issue by comparing three methods. The method used is Linear Discriminant Analysis (LDA), Random Forest, and Binary Logistic Regression. …”
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SBS Suppression Capability of Optimized Pseudo-Random Binary Sequence Phase Modulation in Multi-Stage Fiber Amplifiers by He Wang, Yifeng Yang, Kaiyuan Wang, Qianhe Shao, Xinyu Duan, Xiaolong Chen, Kai Liu, Xiaoqiang Xiong, Junqing Meng, Bing He

“…We demonstrate the capability to suppress stimulated Brillouin scattering (SBS) in a high-power all-fiber laser amplifier system using filtered and amplified pseudo-random binary sequence (PRBS) phase modulation techniques. …”
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An Enhanced Tree Ensemble for Classification in the Presence of Extreme Class Imbalance by Samir K. Safi, Sheema Gul

“…The efficacy of the proposed method is assessed using twenty benchmark problems for binary classification with moderate to extreme class imbalance, comparing it against other well-known methods such as optimal tree ensemble (OTE), SMOTE random forest (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>R</mi><mi>F</mi></mrow><mrow><mi>S</mi><mi>M</mi><mi>O</mi><mi>T</mi><mi>E</mi></mrow></msub></mrow></semantics></math></inline-formula>), oversampling random forest (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi mathvariant="normal">R</mi><mi mathvariant="normal">F</mi></mrow><mrow><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow></msub></mrow></semantics></math></inline-formula>), under-sampling random forest (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi mathvariant="normal">R</mi><mi mathvariant="normal">F</mi></mrow><mrow><mi mathvariant="normal">U</mi><mi mathvariant="normal">S</mi></mrow></msub></mrow></semantics></math></inline-formula>), k-nearest neighbor (k-NN), support vector machine (SVM), tree, and artificial neural network (ANN). …”
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Euclidean Distance-Based Tree Algorithm for Fault Detection and Diagnosis in Photovoltaic Systems by Youssouf Mouleloued, Kamel Kara, Aissa Chouder, Abdelhadi Aouaichia, Santiago Silvestre

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A Multi-Kernel Mode Using a Local Binary Pattern and Random Patch Convolution for Hyperspectral Image Classification by Wei Huang, Yao Huang, Zebin Wu, Junru Yin, Qiqiang Chen

“…In order to improve classification performance while reducing costs, this article proposes a multikernel method based on a local binary pattern and random patches (LBPRP-MK), which integrates a local binary pattern (LBP) and deep learning into a multiple-kernel framework. …”
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Upgrading Frequency Test for Overlapping Vectors and Fill Tree Tests by Krzysztof Mańk

“…This paper we analyze three tests. Starting with a range of observations made for a well-known frequency test for overlapping vectors in binary sequence testing, for which we have obtained precise chi-square statistic computed in O dt 2dt instead of O 22dt time, without precomputed tables. …”
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An effective flowchart for multimodal brain tumor binary classification with ranked 3D texture features by Mücahid Barstuğan

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A binary choice model for adoption of an emerging travel mode with unique service features by Yu Gu, Anthony Chen, Sunghoon Jang, Songyot Kitthamkesorn

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Bundled assessment to replace on-road test on driving function in stroke patients: a binary classification model via random forest by Lu Huang, Lu Huang, Xin Liu, Jiang Yi, Yu-Wei Jiao, Tian-Qi Zhang, Guang-Yao Zhu, Shu-Yue Yu, Zhong-Liang Liu, Min Gao, Xiao-Qin Duan

“…The subject was classified as either Success or Unsuccess group according to whether they had completed the on-road test. A random forest algorithm was then applied to construct a binary classification model based on the data obtained from the two groups.ResultsCompared to the Unsuccess group, the Success group had higher scores on the OCS scale for “crossing out the intact heart” (p = 0.015) and lower scores for “executive function” (p = 0.009). …”
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